Time series models with an EGB2 conditional distribution
AbstractA time series model in which the signal is buried in noise that is non-Gaussian may throw up observations that, when judged by the Gaussian yardstick, are outliers. We describe an observation driven model, based on an exponential generalized beta distribution of the second kind (EGB2), in which the signal is a linear function of past values of the score of the conditional distribution. This specification produces a model that is not only easy to implement, but which also facilitates the development of a comprehensive and relatively straight-forward theory for the asymptotic distribution of the maximum likelihood estimator. The model is fitted to US macroeconomic time series and compared with Gaussian and Student-t models. A theory is then developed for an EGARCH model based on the EGB2 distribution and the model is fitted to exchange rate data. Finally dynamic location and scale models are combined and applied to data on the UK rate of inflation.
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Bibliographic InfoPaper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 1325.
Date of creation: 17 Jul 2013
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Web page: http://www.econ.cam.ac.uk/index.htm
Beta distribution; EGARCH; fat tails; score; robustness; Student's t; Winsorizing;
Other versions of this item:
- Michele Caivano & Andrew Harvey, 2014. "Time series models with an EGB2 conditional distribution," Temi di discussione (Economic working papers) 947, Bank of Italy, Economic Research and International Relations Area.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-07-28 (All new papers)
- NEP-ECM-2013-07-28 (Econometrics)
- NEP-ETS-2013-07-28 (Econometric Time Series)
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